Deconstructing Stereotypes: Scope-Conditioned Generation for Effective Multilingual Counterspeech
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Computer Science > Computation and Language
arXiv:2609.16906 (cs)
[Submitted on 15 Sep 2026]
Title:Deconstructing Stereotypes: Scope-Conditioned Generation for Effective Multilingual Counterspeech
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Abstract:Counterspeech (CS) - direct responses that counter online Hate Speech (HS) using reasoning and alternative viewpoints - has emerged as an alternative to content removal. Current automatic CS generation methods, however, frequently produce generic, ineffective replies that fail to target the implicit stereotypes behind HS. To bridge this gap, we propose a novel scope-conditioned generation framework that explicitly integrates structured stereotype characteristics into Large Language Models prompts. We validate our approach on a novel, human-curated dataset annotated in English, Italian, and Spanish. Extensive evaluations show that stereotype-conditioned prompting substantially outperforms generic baselines across all three languages, obtaining significant gains in factuality, specificity, cogency, and effectiveness for both explicit and implicit implied stereotypes.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.16906 [cs.CL] |
| (or arXiv:2609.16906v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16906
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Deconstructing Stereotypes: Scope-Conditioned Generation for Effective Multilingual Counterspeech, by Greta Damo and 4 other authors
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